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Train Classification Ensemble

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This example shows how to create a classification tree ensemble for the ionosphere data set, and use it to predict the classification of a radar return with average measurements.

Load the ionosphere data set.

load ionosphere

Train a classification ensemble. For binary classification problems, fitcensemble aggregates 100 classification trees using LogitBoost.

Mdl = fitcensemble(X,Y)
Mdl = 
  ClassificationEnsemble
             ResponseName: 'Y'
    CategoricalPredictors: []
               ClassNames: {'b'  'g'}
           ScoreTransform: 'none'
          NumObservations: 351
               NumTrained: 100
                   Method: 'LogitBoost'
             LearnerNames: {'Tree'}
     ReasonForTermination: 'Terminated normally after completing the requested number of training cycles.'
                  FitInfo: [100x1 double]
       FitInfoDescription: {2x1 cell}

Mdl is a ClassificationEnsemble model.

Plot a graph of the first trained classification tree in the ensemble.

view(Mdl.Trained{1}.CompactRegressionLearner,'Mode','graph');

Figure Regression tree viewer contains an axes object and other objects of type uimenu, uicontrol. The axes object contains 36 objects of type line, text. One or more of the lines displays its values using only markers

By default, fitcensemble grows shallow trees for boosting algorithms. You can alter the tree depth by passing a tree template object to fitcensemble. For more details, see templateTree.

Predict the quality of a radar return with average predictor measurements.

label = predict(Mdl,mean(X))
label = 1x1 cell array
    {'g'}

See Also

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